Thematic Mapping Example- Man’s Domain / A Thematic Atlas of the World.
This week’s assignment will encompass the following concepts covered in Class 4 lecture & lab:
Specific techniques covered this week will include:
Note: the map example layout applies to both the primary and extra credit options of the assignment, while the map theme itself is applicable to the extra credit component of the assignment.
This week’s readings will include 2 sections from the Essentials of Geographic Information Systems textbook; further, 3 supplemental technical readings that cover SQL formatting that will increasingly be used throughout assignments, and formulas for normalizing census data.
The class 4 quiz will cover only content from the Essentials of Geographic Information Systems textbook as follows:
Below are links to further readings for those interested in the United States political dimension of ‘MAUP’, Gerrymandering and a divided US electorate:
Topics Covered:
Note: this lab uses the course textbook Discover QGIS 3.x - Exercise 2 - Introduction to Geospatial Analysis, (pp. 111-119); and Statistics | Creating Statistical Summaries | Generating Histograms, (pp. 129-130).
General concepts covered in Class 4 Assignment - Thematic Mapping with US Census Data :
.gdbNote: this week will feature a
CRS‘on the fly projection’ transformation to achieve a better map ‘shape’ for the required assignment map. The image below shows an equal area projection vs. the defaultWGS84projection. The image source is linked to an article that discusses these differences further:
Map Project Options Suitable for US Contiguous States
Source: https://source.opennews.org/articles/choosing-right-map-projection/
In this assignment, one thematic map for Contiguous United States based on a demographic variable derived from the American Community Survey 2018 data ACS_2018_5YR_County (5-year estimates from the 2014-2018 ACS) will be developed. This will require a two-step process: access and prepare the data, followed by a classified mapping of the data.
The top grade for this map will be 90 points. There is an extra credit component for the addtional 10 points. The video guide towards end of assignment will guide you through an example for the extra credit component using the age+sex table. The videos will not cover the cartographic design elements covered in previous classes; make sure to use all the cartography skills you learned to enhance this week’s deliverable - legend, inserts (consider including Alaska and Hawaii and Puerto Rico as map inserts - an example HERE), map titling and data sourcing. Scale bars and north arrows are often not essential for effective thematic mapping- use them sparingly, if at all, when producing thematic maps.
Note: including map insets for Hawaii, Alaska, Puerto Rico and other county census geographies not in the lower 48 is not required of this assignment - only if you prefer to add them to your map layout.
Navigate to data and download bundled product TIGER/Line with Selected Demographic and Economic Data via the following link:
US Census
Utilize American Community Survey 5-Year Estimates — Geodatabase Format 2014-2018
Select County > Download Data:
Download Census Data
ACS_2018_5YR_COUNTY.gdb.zip. make sure to keep the resulting directory with folder extension intact: ACS_2018_5YR_COUNTY.gdb:.gdb format
.gdb are available for import into QGIS. However, we only need the geometry for the county boundaries plus one theme - for this mapping, population counts that will then become population densities per county in the final mapping. We need to know which table to import. To do this, we utilize an online html document - ACS variables 2018 that lists all the acs tables and their themes. There are over 64,000 variables in this dataset, so isolating the correct table is an essential first step. source: https://api.census.gov/data/2018/acs/acs5/variables.html
source: https://api.census.gov/data/2018/acs/acs5/variables.html
For the assignment mapping, we will use the SEX BY AGE table B01001_001E. Here we will use just the first variable, which is the total population estimate per county - B02001e1:
B01001e1|SEX BY AGE - Universe: Total population - Total: -- (Estimate)Census Theme in Table
.gdb to begin mapping. We will discuss the .gdb format during Class 4 Lecture and Lab. Further, see video references at end of assignment for .gdb imports to QGIS. Select both the geometry and table B01001_001E - SEX BY AGE:Connect to .gdb
Connect to .gdb - feature + tabular data
Connect to .gdb - feature + tabular data
AGE AND SEX will be exported as .csv outward from the .gdb structure. As this is done, the table will be ‘thinned’ to the just those variables needed for the mapping - the OBJECTID, GEOID and B01001_001E alone. Save the export as acs.2018.population.csv into the assignment project folder directory. Also state No geometry as geometry type: 10. Next, import
acs.2018.population.csv as delimited text:
Delimited Text
ALAND variable in the dataset which equates to square meters for each county. To calculate area units - ALAND as Square Miles - the following calculation is used:1 sq. mile = 2,589,988.110336 metersALAND/2589988.110336.gdb to a .shp and title acs.geo.shp. Make sure NOT to change the coordinate system which is NAD83 - EPSG:4269:.shp Export
sq.mile:Field Calculator
acs.geo and the tabular data acs.2018.population. As is, these two data files exist side-by-side in the QGIS project. We need to ‘join’ these two files based on a common attribute. This is known as a ‘table join’. To start, save the project to update to the current data files:Table Join Preparation
GEOID that is the US Census unique identifier across all census geographies:Table Join Preparation
asc.geo = GEOID_DATATable Join Preparation
asc.2018.population = GEOIDasc.geo, navigate to Properties > Joins > green plus button and populate as follows:Table Join Preparation
asc.2018.population now joined correctly to the asc.geo layer (far right field in image below). This table join is currently loaded in temporary memory. It must now be exported as a new .shp before proceeding:Table Join Preparation
acs.2.map.shp:.shp Export
.shp Export
.shp Export
.shp Export - Result
acs.2.map.shp feature. Create a new field via the Field Calculator and populate as follows. Proceed to Toogle editing OFF and save the new field:Population Density Calculation in Field Calculator
Note: a Whole Number (integer) field type is selected as persons can only be whole numbers, not decimal numbers, i.e. there are only whole persons, not partial persons. There are 69 counties that contain less than 1 person per sq. mile. These counties will simply receive a
0and will be classed accordingly in the final thematic map.
pop.den > Classify button at bottom:Graduated Symbology - Equal Count - Quantile
Cartographic Result
Graduated Symbology - Natural Breaks
Cartographic Result
Produce final Map layout and design. Output as PDF 300 DPI 8.5“x11” or 11“x17” (use .png or .tiff if PDF at 300 DPI produces too large file size export). If pursuing the extra map II, follow the guidelines provided below, and again, produce map layout, design and output similar to the main map assignment.
You are strongly encouraged to pursue the extra credit portion of the assignment for a top potential score of 100 points. While the required map above will feature basic demographic data at the county level, the extra credit map will feature a more tailored exploration of census data. In this extra credit mapping, you will utilize the same data source bundled US Census ACS product. Instead of normalizing the data by areal units (population density per sq. miles), you will normalize the census theme by the theme universe population per US county.
The equation for this normalization: census theme count/census universe population*100
US Census Links:
Online tools & utilities to aid thematic map design:
Helpful articles and resources for census data and thematic mapping techniques:
Case Study - The Marshall Project:
The Marshall Project extracted the number of adults in correctional facilities per county from the 2000, 2010 and 2020 Decennial Census.
U.S. County distribution of Incarcerated Populations
Data Fields in the Dataset